Remote infrastructure awareness for decision making requires merging various data sources, mainly visual inspections, which allow addressing a wide infrastructure portfolio and is an essential component of current infrastructure management practice; Direct SHM, which is currently reserved for critical infrastructure within the whole portfolio; and 3D geometry representation. The evidence (images, descriptions, and criticality levels) gathered from visual inspections, coupled with the geometric context of the asset allows to arrive at much evident insights of infrastructure behaviour. This work describes the architecture and operation of an asset-centered infrastructure-monitoring platform by LIND Engineering. The platform models the asset record as a canonical anchor with spatial references and lifecycle metadata. From this anchor, the platform orchestrates four capabilities: (i) visual inspection, structured as evidence + metadata + 3D location with configurable criticality, versioned records, and chain-of-custody to support auditable decision flows; (ii) direct SHM integration, in which asset-referenced time series are ingested and aligned to the asset context for subsequent analysis and event extraction via declarative rules; (iii) indirect monitoring, accommodating pass-by/vehicle-borne signals referenced to the asset and route for complementary condition awareness; and (iv) 3D spatial context, allowing multiple independent representations per asset (e.g., a semantic model or a point cloud) without requiring fusion; each finding is anchored to the selected representation, preserving its spatial reference across version updates of that same representation. At the platform level, LIND implements multitenancy, role/attribute-based access control, client-level segregation, provenance, and auditable exports that retain spatial and temporal references. Field operations emphasize on-site capture with reliable synchronization under limited connectivity, whereas web-scale 3D visualization employs progressive loading to sustain performance and readability in large scenes. This contribution is a replicable, operational, asset-centric architecture that unifies visual inspection, direct/indirect sensor data, and 3D spatial representations to enable reproducible risk prioritization, auditability, and scalable deployment in real-world SHM.